In this extended abstract, we summarize our recently published work on CNN textural bias in the context of MRI k-space artifacts, namely Gibbs, spike, and wraparound artifacts. We illustrated how carefully simulating artifacts at training time can help reduce textural bias, and consequently lead to CNN models that are more robust to acquisition noise as well as out-of-distribution inference, including data from previously unseen hospitals. We also introduced Gibbs ResUnet; an end-to-end framework that automatically finds optimal combinations of Gibbs artifacts and segmentation model weights. The work was carried out on multimodal and multi-institutional clinical MRI data obtained retrospectively from the Medical Segmentation Decathlon (n=750) and The Cancer Imaging Archive (n=243).